217 results about "Binary tree" patented technology

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention relates to the technical field of distributed data processing systems, and discloses a data processing system supporting full localization deployment and a compliance guarantee method thereof.The data processing system supporting full localization deployment comprises an audit event collection module used for collecting and storing processing event hash; the event organization module is used for realizing logic time sequence management of events through topological sorting and causal relationship marking; the hash chain construction module is used for constructing a local-to-global double-layer hash chain by utilizing the constructed directed acyclic graph and event hash; the increment synchronization module is used for executing increment synchronization based on Hash tree difference identification; the conflict solving module is used for detecting and solving event conflicts in the distributed environment by applying a version vector technology aiming at data differences found in the incremental synchronization process; according to the method, the lightweight linear hash chain is used for replacing a traditional binary tree structure, high-efficiency and low-cost audit chain maintenance is realized, and the data integrity is guaranteed in an unstable network environment.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention discloses an ALB sounding point cloud filtering and recovering method for complex terrains such as a submarine abrupt slope, belongs to the technical field of data processing, and solves the problems that the I-class misjudgment rate is high, topographic features are smoothed or weakened and effective point clouds at the submarine abrupt slope are easily filtered out excessively in an existing method. The method comprises the steps of performing sea surface elevation estimation and separation processing on original point cloud, processing underwater point cloud based on a self-adaptive morphology water body filtering method, introducing a recovery mechanism based on hyperboloid fitting, recovering abrupt slope area point cloud misjudged as a water body noisy point through residual analysis, and obtaining a water body noisy point. According to the method, a fine processing strategy of filtering first and then recovering is adopted, and water body noisy points are accurately removed through self-adaptive morphological filtering in the earlier stage in combination with grid reference surface and spatial multi-dimensional binary tree index neighborhood analysis. A recovery mechanism based on hyperboloid fitting is introduced, and the abrupt slope area point cloud misjudged as the noisy point is recovered through residual analysis. And the misjudgment rate in a submarine abrupt slope area is effectively reduced.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The present application relates to the technical field of video coding, and especially relates to a coding unit division fast decision method and system based on multi-functional video coding, comprising constructing a quadtree division prediction network, using the network to judge whether a coding tree unit needs to perform quadtree division, and obtaining a coding unit after performing the quadtree division; constructing a mixed tree division prediction network, using the network to judge whether the coding unit needs to be divided, stopping division if the coding unit does not need to be divided, otherwise judging whether the coding unit adopts binary tree division or ternary tree division, and taking a smaller coding unit division prediction value in parallel branches as a final prediction result. The present application improves coding efficiency, and can balance the relationship between visual quality and coding efficiency by selecting different threshold values according to specific scene needs.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention provides a video coding method, which comprises the following steps of: inputting a coding tree unit to be coded, and extracting texture features of a coding block, the texture features comprising local binary pattern similarity of all pixel points in the coding block and direction dispersion of each pixel point; inputting the texture features into the trained multi-type division prediction architecture, and sequentially judging whether to execute quadtree division, horizontal or vertical division, horizontal binary tree or ternary tree division and vertical binary tree or ternary tree division or not through a binary classification problem method; and calculating the rate distortion cost under each division mode to obtain an optimal division mode and a suboptimal division mode of the coding block, and outputting an optimal mode or a combination of the optimal mode and the suboptimal mode. According to the invention, the accuracy of the prediction mode in coding is improved.

The application discloses an inadvertent random access machine storage system based on a double-buffer data read-write algorithm, which comprises a cloud storage unit and a user end, wherein the cloud storage unit is provided with a full binary tree for storing encrypted data; the user end is provided with a first buffer, a second buffer and a position table located at the user end; a part of the top of the full binary tree is not used for storing data, so that the full binary tree comprises m full binary tree sub-trees.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention provides a double-server combined keyword dynamic searchable encryption method with forward and backward security, which is applied to a system comprising a data owner, a data user, a first server and a second server. Keywords and associated file identifiers are stored in each leaf node, information of non-leaf nodes is mapped to a non-distinguishable long filter, and through a random state updating mechanism, a keyword double-state array and a time factor trap door design, it is guaranteed that keyword joint search is achieved, and meanwhile, the information of the non-leaf nodes can be quickly and efficiently searched. And the forward safety and the backward safety are also ensured. According to the dual-server architecture, security index storage and trap door verification are separated, so that the problem of KPRP leakage is effectively relieved.

In some implementations, a memory apparatus may receive a command indicating a logical block address (LBA) that is associated with a logical-to-physical (L2P) table. The memory apparatus may perform a lookup operation associated with a compressed version of an address range of the L2P table to identify a physical address in non-volatile memory associated with the LBA, wherein the compressed version is stored in a volatile memory of the memory apparatus, wherein the compressed version is associated with an exception list that indicates physical addresses for respective LBAs, included in the address range, that are associated with non-sequential physical addresses, and wherein the compressed version is associated with a binary tree that indicates locations in the exception list associated with the respective LBAs. The memory apparatus may perform, based on the command, an action associated with the physical address.

A content channel generation device includes: a splitting circuit for generating resource units according to resource assignments; a resource unit assignment circuit for generating a full binary tree according to the resource units; a node computing circuit for generating a minimum full binary tree according to the full binary tree; a load balance circuit for generating user field numbers corresponding to content channels according to the minimum full binary tree and a load balance function; a merging circuit for generating a traversal result of the minimum full binary tree according to a traversal algorithm, and for generating a merged traversal result according to the traversal result; and a common field generation circuit for generating a resource assignment indicator according to the merged traversal result to generate a common field.

The invention discloses a hardware system for calculating an SLHDSA binary tree, an SLHDSA binary tree calculation method, a storage medium and equipment, and belongs to the field of post-quantum algorithms. A binary tree controller; a plurality of first memory cells; the plurality of second storage units are used for calculating a root node value of a four-layer binary tree at the bottom of the eight-layer binary tree through a plurality of hash modules for each of all the eight-layer binary trees at the bottom of the SLHDSA binary tree; writing a root node value of the four-layer binary tree into a plurality of first storage units through a binary tree controller; calculating the root node value of the eight-layer binary tree by using the root node values read from the plurality of first storage units through a hash module; writing a root node value of the eight-layer binary tree into a plurality of second storage units through a binary tree controller; and calculating a root node value of the SLHDSA binary tree by utilizing the root node values read from the plurality of second storage units through a hash module. According to the method, the calculation efficiency of the high-level binary tree is improved, and the consumption of storage resources is reduced.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The application provides a quick retrieval method for language structure big data, according to the type of language structure data, the basic index unit to be constructed and the index content corresponding to the index unit are determined, when retrieving, the retrieval expression input by the user is analyzed, the retrieval unit corresponding to the index unit and the relationship between the retrieval units are analyzed, the result of each retrieval unit is obtained in the way of single chain binary tree postorder traversal and is combined according to the relationship between the analyzed retrieval units. In the application, the design of the index unit can utilize the lexical and syntactic information of the language data itself, realize the retrieval requirement of multi-directional language knowledge of the language structure data, the design of the structure retrieval language can enable the user to learn the retrieval language at a low cost and write the retrieval expression which is standard and meets the retrieval requirement.

A method for verifying the integrity of a binary tree graph structure having "h" layers and a plurality of nodes configured to provide public keys, the method comprising: verifying the integrity of a stored root hash value associated with a root node; obtaining non-root nodes among the plurality of nodes, the "h" layers including leaf layers, and the leaf layers including a plurality of leaf nodes associated with a plurality of one-time signature public keys; obtaining an authentication path associated with the non-root node, the authentication path including other nodes; performing multiple pairwise hash calculations on the non-root node and the other nodes to generate candidate root hash values; comparing the candidate root hash values ​​with the stored root hash values; and determining that the integrity of the binary tree graph passes verification when the candidate root value equals the stored root value.

This application provides a batch authentication and dynamic group key negotiation method for vehicle-to-everything (V2X) networks. The method includes: in the third-party authentication phase, a roadside unit (Roadside Unit) aggregates multiple vehicle authentication requests for batch verification by a trusted center, and encodes the authentication response and group key calculation parameters into a single composite value using the Chinese Remainder Theorem for broadcasting. Vehicles extract verification information and their unique parameters through modulo operation to complete bidirectional authentication. In the dynamic group key negotiation phase, the Roadside Unit maps authenticated vehicles and itself as binary tree leaf nodes, recursively generating a public group key based on the node's public key and unique parameters. When member changes, only the affected path node information is locally updated. This application integrates authentication response and key negotiation information, reducing downlink channel bandwidth usage and decreasing the group key update complexity from linear to logarithmic levels. It ensures forward and backward security while meeting the real-time communication requirements of high-concurrency access and highly dynamic topology scenarios in V2X networks.

The present disclosure relates to the technical field of memory management, and particularly relates to a cache line replacement method and device. The method is used for implementing replacement search of a cache line in a data block partition, and the method comprises the following steps: receiving a replacement request, determining a root node in a binary tree structure corresponding to the data block partition as a first target node and obtaining node information of the first target node; if the target node is a non-leaf node, determining a child node of the target node in a replacement direction indicated by a processing state of the current target node as a next target node and updating a target counter of the target node, until the determined target node is a leaf node, and then determining a data block to be searched based on the leaf node; searching out a replaceable cache line in the data block to be searched by using a PLRU algorithm; and generating a search result according to a data block ID of the data block to be searched and a cache line ID of the replaceable cache line. The cache storage capacity is increased, the time required for hit test is reduced, and balanced cache line replacement is realized.

This application provides a cross-border guarantee data processing method and apparatus, relating to the field of blockchain technology, and applicable to the financial and other fields. The method includes: configuring corresponding shared parameter information for binary tree shared path modeling based on guarantee transaction data provided by a user to a first bank application server, through a blockchain node server associated with the first bank application server; calculating the corresponding binary tree shared path and shared guarantee data based on the shared parameter information; synchronizing and storing the binary tree shared path, the shared guarantee data, and the corresponding calculated binary tree root hash to a blockchain network constructed by multiple banks through corresponding blockchain node servers; obtaining the shared guarantee data from the blockchain network through a blockchain node server associated with a second bank application server; and providing the guarantee data to the second bank application server for guarantee credit processing.

A N-channel interleaved analog-to-digital converter (ADC) adds a variable delay to the input sampling clock of each ADC. During calibration, each variable delay is edited by a successive approximation register (SAR) to minimize the time phase mismatch between channels. An autocorrelator generates a sign of a correlation error for a pair of ADC digital outputs. The SAR bits are tested using the correlation sign to determine when to add or subtract the SAR bits. Calibration is first performed in a binary tree first layer of a multiplexed correlator. Then, the deviation between more distant pairs and groups is calibrated in higher layers of the binary tree using an autocorrelator whose inputs are multiplexed from the ADC output groups to the binary tree of the multiplexed correlator. The binary tree of the multiplexed correlator can include bypassers for odd non-binary values of N. The sampling clock and component time phase mismatch between two adjacent channels and more distant channels is reduced to 1 LSB.

The invention discloses a model training method and device, and relates to the technical field of artificial intelligence. A specific embodiment of the method comprises the following steps: pre-configuring a plurality of initial features of a specific article of a target category, and determining an evaluation score of the plurality of initial features in at least one evaluation index according to a pre-trained feature evaluation model; screening out at least one key feature from the plurality of initial features according to the evaluation score, and training a pre-established grading model by using the training sample data of the specific article based on the key feature; the grading model is a random forest model comprising a plurality of composite decision-making trees, any composite decision-making tree comprises a top decision-making tree and two bottom decision-making trees, and the top decision-making tree and the bottom decision-making trees are binary trees; the root nodes of the two bottom-layer decision trees in the same composite decision tree are leaf nodes of the top-layer decision tree. According to the embodiment, automatic and refined grading of the specific article can be realized based on the artificial intelligence model.

The invention relates to the field of data query, and discloses a space grid data query method based on AABB and KDTree composite index, comprising the following steps: step 1, acquiring simulation data of a part, establishing a space mapping relationship between the simulation data and a geometry of a die casting, and performing AABB space division on the geometry, the AABB space including primary AABB space division and secondary AABB space division; the primary AABB space division divides the geometry of the die casting into a plurality of topologically associated sub-regions; the sub-region is divided into a plurality of tetrahedron units by the secondary AABB space division; performing binary tree structure recombination on the tetrahedron units in the sub-regions by adopting KDTree; and 2, judging whether the target coordinate point is in the sub-region or not, traversing the tetrahedron unit of the sub-region, and remarkably reducing the calculation complexity and improving the spatial query efficiency of the large-scale grid model by introducing a spatial index structure and an algorithm optimization strategy.

Acceleration of S-polarized ECC throughput by a scheduler is disclosed. A method of simplified successive cancellation list (SSCL) error decoding of an S-polar code includes representing an S-polar code as a perfect binary tree; providing a node v with a soft information vector from a parent node of v to compute a soft information vector for a left child node of v; providing v with a hard decision vector from the left child node and using it with to create a soft information vector and pass it to a right child node of v; providing v with a hard decision vector from its right child node and using it with to create a hard decision vector β v and pass it to its parent node; when v is an i-th leaf node of the perfect tree, updating two path metrics and selecting a path obtained by extending a current path by the lowest path metric.

Several methods are described to jointly use the ABT (Asymmetric Binary Tree) partitioning mode and Matrix-based Intra Prediction (MIP). In a first embodiment, we propose to forbid the use of the MIP intra prediction mode, for block sizes that are resulted from ABT partitioning. In a second embodiment, we propose to allow the MIP intra prediction for block sizes not equal to a power of two in width or height, by extending the block before MIP and crop the predicted block to the original size after MIP. In a third embodiment, we propose to adapt the down-sampling of the boundary reference samples and the up-sampling of the reduced predicted blocks, to the block sizes introduced by ABT partitioning. In a further embodiment, we set the reduced predicted block to size 8×8 in any case the initial block size is 8 and larger than 8 in a direction.

The application provides a data prediction model creation method, a prediction method, an apparatus and a storage medium. The model creation method comprises: an initiator device obtaining an initial first sample set of a root node of a binary tree structure, the first sample set comprising samples common to the initiator device and a participant device; determining how to divide the first sample set of the root node according to sample division evaluation parameters of the first sample set at the initiator device and the participant device; dividing sample sets of child nodes of the root node based on the same principle until the sample division evaluation parameters of the sample sets in all child nodes are less than or equal to a threshold value, stopping sample division, and completing creation of the data prediction model. The above process comprehensively considers sample division evaluation parameters of sample sets of nodes by all parties, determines whether to divide and how to divide the sample sets of the nodes based on the sample division evaluation parameters, and the prediction result of the data prediction model created by the method is more accurate.